A General Model Transformation Methodology to Serve Enterprise

A General Model Transformation Methodology to Serve Enterprise

Interoperability Data Sharing Problem

Centre Genie Industriel - Ecole des Mines d'Albi

28/05/2015

Tiexin WANG

Sebastien TRUPTIL

Frederick BENABEN

Content

1. Problematic

2. Related Work

3. Methodology Overview

4. Semantic and Syntactic Measuring

5. Conclusion

1 28/05/2015 Tiexin WANG

Background


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Before: small & local group of partners, long period, static combination

Now: big/small global group of partners, short period, dynamic combination [Touzi and al, 2007]

New requirement: fast and efficient data exchange among heterogeneous partners

Common

Goal

The ability of enterprise to cooperate with other enterprises: interoperability

Interoperability


Problematic

Related Work

General Overview

S & S measuring

Conclusion

[Konstantas, 2005] The ability of a system or a product to work with other systems or products without

special effort from the user; it is a key issue in manufacturing and industrial enterprise.

Two definitions:

[Ide and Pustejovsky, 2010] A measure of the degree to which diverse systems, organizations, and/or

individuals are able to work together to achieve a common goal. For computer systems, interoperability

is typically defined in terms of syntactic interoperability and semantic interoperability.

[Chen and al, 2010] Enterprise Interoperability Framework (EIF) was proposed: models serves interoperability.

Integrated approach: a common format for all the models Unified approach: a common format for models on meta-level Federated approach: no common format for models

This project: focuses on: unified & federated approaches serves to: conceptual to technological transform considers: data and part of service concerns

achieve interoperability Pre-efforts required from users

easy huge

hard large

difficult none

Model & Meta-model


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Organization

model

Financial

model

Production

model Information

model

……

Model: could be seen as a picture of a system, depending on a point of view. This picture is a

simplification of this system, which highlights its characteristics. [Bézivin, 2006]

Meta-model: it is also a model; it defines the rules of building models.

Modeling

Real system

Model

presents

Meta-Model

Meta-Meta-Model

conforms

conforms Models & Meta-

models

Meta-Models

Real system Model

control

information

Model transformation


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Simple examples of model transformation:

Design Coding

IDEF model BPMN model

[Del Fabro, 2008] Weaknesses of traditional model transformation practices

low reusability

contains repetitive tasks

involves huge manual effort

……

How to ensure data exchange in a unified and federated approaches of interoperability ？

Express data based on models

Define an automatic model transform methodology

MT theories


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Target

Meta-Model

Source

Meta-Model

Specific

Part

Extracted

knowledge

Capitalized

knowledge Transformed

knowledge

Additional

knowledge

Source

Model Target

Model

Backup

Enrichment

Special

Concepts Special

Concepts

Specific

Part Shared

Part

Shared Concepts

Shared

Part

Mapping rules

Meta-meta model

Created based on [Bénaben, 2010]

Semantic & syntactic checking

Automatically defined

The main framework

Techniques & practices


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Involving semantic and syntactic checking measurements into model

transformation process to automatically define mapping rules (shielding

weaknesses); serving to modern enterprise collaboration.

name declarative or

imperative domain specific

self-executed

note

ATL (Atlas transformation language) [Jouault, 2007]

both no yes difficult to use

QVT (Query, View, Transformation language) [OMG]

both no no Based on MOF 2.0

[OMG]

VIATRA2 declarative no yes Graph rewriting

GReAT [Karsai, 2003] both yes yes UML models

name technique domain specific

note

Applying MDE to the semi-automatic development of model transformations

MeTAGeM no viewing model transformations

as transformation models

Meta-model-Based Model Transformation with Aspect-Oriented Constraints

OCL Yes Graph rewriting

GMTM

S&S

no

Automatic execute

Scientific contribution of this project:

GMTM


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Theoretical Solution

Focus on

element & property Source Meta-model

Source Model

target Model

Target Meta-model

Mappings

semantic

comparison

syntactic

comparison

Model A

Model B

Model C

Model D

Model E

Focus on

model instances

Validate Define new

Theoratical solution


Problematic

Related Work

General Overview

S & S measuring

Conclusion

ontology

Unmatched items base

……

SM TM

SM: source model TM: target model CK: capitalized knowledge AK: additional knowledge

……

……

CK AK CK

AK

SM TM

SM

TM SM

TM

CK

AK

: element

: property

Model transformation: iterative process

structure

Matching mechanism


Problematic

Related Work

General Overview

S & S measuring

Conclusion

E A2

E A1

E B1

E B2

…

Elements

Source Meta-Model: A Target Meta-Model: B

Mappings

E A3

…

name type

Properties

Element -- Element

Property -- Property

Element -- Property

Property -- Element

Element A1

Element B1

name

Property

Syntactic & semantic checking

elements' & properties' names

properties Elements

name

S & S comparing


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Semantic & syntactic comparison:

names: student -- person

properties:

name: string surname: string

age: integer forename: string

address: string gender: string

sex: string address: string

S_SSV stands the semantic and syntactic relation between two words (strings). Its value is between 0 and 1;

name_weight, property_weight are impact factors; the sum of them is 1.

S_SSV = sem_weight*S_SeV + syn_weight*S_SyV (2)

“S_SyV” stands for the syntactic similarity value between two words. “S_SeV” stands for the semantic

value between the two words. “sem_weight” and “syn_weight” are two factors, the sum of them is “1”.

P_SSV = pn_weight*S_SSV + pt_weight*id_type (3)

“P_SSV” stands for semantic and syntactic value for properties; pn_weight, pt_weight are effect factors.

Sum of pn_weight and pt_weight is 1.

Syntactic checking


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Predefined treatment

aims at finding two words that in different forms but stand for same words:

Porter stemming algorithm

son s o n

sun 0 1 2 3

s 1 ABS?

u 2

n 3

son s o n

sun 0 1 2 3

s 1 0 1 2

u 2 1 0 1

n 3 2 1 1

S_SyV = 1 – LD / Max (str1.len, str2.len)

“S_SyV” stands for the syntactic similarity value between two strings. “LD”

means the “Levenshtein distances” between str1 and str2.

“Levenshtein Distances” algorithm

It calculates the syntactic similarity between two words; it is equal to the number of

operations “insertions, deletions and substitutions” that needed to transform on word to another.

An example: compare the syntactic similarity between “son” and “sun”

Semantic checking


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Semantic thesaurus a huge semantic thesaurus which contains large amount of words, is created based on the basis of “WordNet” [Huang, 2007]

word

man

address

word sense

word sense

word sense

word sense

Synset

Synset

Synset

SenseKey

SenseKey SenseKey

SenseKey

SenseKey

Belong

Belong

Belong

Belong

Word Base Sense Base Synset Base

Semantic

relation

Semantic

relation

Word base: contains normal English words (nouns, verbs and adjectives).

Sense base: contains all the word senses; a word could have “one or several” senses.

Star: six senses; as noun, it has four senses; as verb, it has another two senses

"Synset" base: a group of word senses that own synonym meanings; semantic relations are built

among different synsets.

one to many one to one

many to one

Semantic checking


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Semantic relations

Semantic relation S_SeV Example

synonym 0.9 maker & producer

similar-to 0.85 perfect & ideal

hypernym 0.8 creator & maker

antonym -1 good & bad

iterative hypernym 0.8n person – creator – maker

–author

"S_SeV" stands for "semantic value between two words"; its value ranges from 0 to 1. It is

determined by the semantic relation that existed between the two words.

surname forename gender address

name 0.8936 0.888 0.2136 0.7946

age 0.0229 0.02 0.4856 0.2229

address 0.2 0.21 0.6366 1

sex 0.2114 0.21 0.8616 0.6366

student people

name : surname

name : forename

address: address

sex : gender

age ---> specific part

Result of this use case

Conclusion


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Problematic to solution

General problem: fast & efficient exchange information

interoperability & EIF: model based solution

Specific problem: automatic model transformation methodology

S_SyV = 1 – LD / Max (str1.len, str2.len)

S_SSV = sem_weight*S_SeV + syn_weight*S_SyV (2)

P_SSV = pn_weight*S_SSV + pt_weight*id_type (3)

Semantic & syntactic measuring

Prospect


Problematic

Related Work

General Overview

S & S measuring

Conclusion

Future work

•The impact factors such as: sem_weight and pn_weight: assigned values base on intuition and

experience now; using some mathematic strategy (“choquet” integral?) to assign these values?

•Semantic checking measurement: only formal English words (in simple case) are stored in the semantic

thesaurus; other words that in special forms have no semantic meanings in this thesaurus.

•The S_SeV values: more test cases are needed to modify these values into reasonable scope.

• The threshold values to choose matching items’ pairs needed to be more reasonable.

Reference


1. Touzi J, Lorré J P, Bénaben F, et al. Interoperability through Model-based Generation: The Case of the Collaborative Information System

(CIS)[J]. Enterprise Interoperability, 2007: 407.

2. Konstantas D, Bourrières JP, Léonard M, Boudjlida N. Interoperability of Enterprise Software and Applications. IESA’05, Springer-Verlag;

2005.

3. Douglas C. Schmidt. Model-Driven Engineering. IEEE Computer, February 2006 (Vol. 39, No. 2) pp. 25-31.

4. Bézivin, J., 2006. Model Driven Engineering: An Emerging Technical Space. Generative and transformational Techniques in software

Engineering Lecture Notes in Computer Science Volume 4143, pp 36-64.

5. Del Fabro, M.D., Valduriez, P., 2008. Towards the efficient development of model transformations using model weaving and matching

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8. Jouault, F., Allilaire, F., Bézivin, J., Kurtev, I., 2007. ATL: A model transformation tool. Science of Computer Programming. Volume

72,Volume 72, Issues 1–2.

9. OMG. MOF 2.0 Query/View/Transformation (QVT), V1.0, OMG Document –formal/08-04-03.

10. Object Management Group, MOF 2.0 Query / Views / Transformations RFP. 2002, OMG Document.

11. G. Karsai, A. Agrawal, F. Shi, J. Sprinkle, On the use of graph transformation in the formal specification of model interpreters, J. Univ.

Comput. Sci. 9 (11) (2003) 1296–1321.

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13. Huang, X., Zhou, C., An OWL-based WordNet lexical ontology. Journal of Zhejiang University, 2007, pp. 864-870.

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A General Model Transformation Methodology to Serve Enterprise